Prediction of ship track based on ARIMA-LSTM

Chen Yu; Yuhui Fu

doi:10.1117/12.2624397

6 February 2022 Prediction of ship track based on ARIMA-LSTM

Chen Yu, Yuhui Fu

Proceedings Volume 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021); 120812Y (2022) https://doi.org/10.1117/12.2624397
Event: Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 2021, Chongqing, China

Abstract

In order to improve the accuracy and stability of ship trajectory prediction, A combined prediction method based on the differential autoregressive moving average model and the bidirectional cyclic neural network is proposed. The method uses the ARIMA model to make a preliminary prediction of the track, and then uses the LSTM neural network to correct the residual sequence. The experimental results show that this kind of prediction method can predict the ship's trajectory more accurately.

Citation Download Citation

Chen Yu and Yuhui Fu "Prediction of ship track based on ARIMA-LSTM", Proc. SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 120812Y (6 February 2022); https://doi.org/10.1117/12.2624397

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